A powerful finite mixture model based on the generalized Dirichlet distribution: unsupervised learning and applications

Author

Bouguila, Nizar ; Ziou, Djemel

Author_Institution

Sherbrooke Univ., Que., Canada

Volume

1

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

280

Abstract

This paper presents a new finite mixture model based on a generalization of the Dirichlet distribution. For the estimation of the parameters of this mixture we use a GEM (generalized expectation maximization) algorithm based on a Newton-Raphson step. The experimental results involve the comparison of the performance of Gaussian and generalized Dirichlet mixtures in the classification of several pattern-recognition data sets.

Keywords

Gaussian processes; Newton-Raphson method; optimisation; pattern recognition; unsupervised learning; Gaussian mixture; Newton-Raphson method; finite mixture model; generalized Dirichlet distribution; generalized Dirichlet mixture; generalized expectation maximization algorithm; pattern recognition data sets; unsupervised learning; Character generation; Covariance matrix; Image processing; Machine learning; Machine learning algorithms; Maximum likelihood estimation; Parameter estimation; Pattern recognition; Statistical distributions; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334107

Filename

1334107